Evidence map›Paper›PMID 40715116›Full record

ArticleNPJ breast cancer2025

A 20-feature radiomic signature of triple-negative breast cancer identifies patients at high risk of death.

Humaira Noor, Yuanning Zheng, Adam B Mantz, Ryle Zhou, Andrew Kozlov, Wendy B DeMartini, Shu-Tian Chen, Satoko Okamoto, Debra M Ikeda, Melinda L Telli and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ breast cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Humaira NoorStanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Yuanning ZhengStanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Adam B MantzDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Ryle ZhouDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Andrew KozlovDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Wendy B DeMartiniDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Shu-Tian ChenDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Satoko OkamotoDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Debra M IkedaDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Melinda L TelliDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Allison W KurianDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
James M FordDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Shaveta VinayakDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Mina SatoyoshiStanford University Research Technology/Data Services, Stanford, CA, USA.
Vishal JoshiDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Sarah A MattonenDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Kevin LeeDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Olivier GevaertStanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
George W SledgeDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Haruka ItakuraDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA. itakura@stanford.edu.

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI MICHAEL KENNEY · 2007 to 2026
$71.4M
Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Multi-scale data integration frameworks to improve cancer outcomesK01ES026832 · NIEHS · STANFORD UNIVERSITY · PI ITAKURA, HARUKA · 2015 to 2020
$955k
NCATS NIH HHS UL1 TR003142NCI NIH HHS HHSN261201800009CNCI NIH HHS HHSN261201800009INCI NIH HHS HHSN261201800015CNCI NIH HHS HHSN261201800015INCI NIH HHS HHSN261201800032CNCI NIH HHS HHSN261201800032INCI NIH HHS P30 CA124435NIEHS NIH HHS K01 ES026832
6 · The paper itself

Abstract

A substantial proportion of patients with non-metastatic triple-negative breast cancer (TNBC) experience disease progression and death despite treatment. However, no tool currently exists to discriminate those at higher risk of death. To identify high-risk TNBC, we conducted a retrospective analysis of 749 patients from two independent cohorts. We built a prediction model that leverages breast magnetic resonance imaging (MRI) features to predict risk groups based on a 50-gene Transcriptomics Signature (TS). The TS distinguished patients with high-risk for death in multivariate survival analysis (Transcriptomic cohort: [HR] = 13.6, 95% confidence interval [CI] = 1.56-1, p = 0.02; SCAN-B cohort: HR = 1.45, CI 1.04-2.03, p = 0.02). The model identified a 20-feature radiomic signature derived from breast MRI that predicted the TS-based risk groups. This imaging-based classifier was applied to a validation cohort (log rank p = 0.013, accuracy 0.72, AUC 0.71, F1 0.74, precision 0.67, and recall 0.82), detecting a 25% absolute survival difference between high- and low-risk groups after 5 years.

Identifiers

PMID40715116
PMCPMC12297616

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.